Graduation-FCAI-V2

• to run the program run this uvicorn main:app --host 0.0.0.0 --port 8000 --reload A comprehensive AI system that combines multiple components for face recognition, voice-to-text conversion, and retrieval-augmented generation (RAG).

Components

1. Face Recognition

Located in /face_recognition2/

• Facial recognition system with model training capabilities
• Image capture functionality
• MVC architecture with controllers, models and helper utilities

2. Voice to Text

Located in /voice2txt/

• Speech-to-text conversion system
• MVC architecture with Controllers, Models and Services

3. RAG Models

Located in /rag_model/

• Two versions of RAG implementation:
  • agno_rag_v00: Initial implementation
  • Agno_RAG_v01: Enhanced version with authentication and web interface
• RESTful API routes
• Service-based architecture

4. Middleware

Located in /middle_ware/

• Data pipeline implementation
• Integration layer between components

Setup

1. Clone the repository

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Environment Configuration:

• Copy .env.example to .env
• Configure the environment variables according to your setup

Project Structure

.
├── face_recognition2/        # Face recognition module
│   └── src/
│       ├── controllers/     # Recognition controllers
│       ├── helpers/        # Utility functions and configurations
│       └── models/         # Facial recognition models
├── middle_ware/            # Integration layer
│   └── src/
│       └── data_pipeline.py
├── rag_model/             # RAG implementations
│   ├── agno_rag_v00/     # Initial version
│   └── Agno_RAG_v01/     # Enhanced version with web interface
└── voice2txt/            # Speech-to-text module
    └── src/
        ├── Controllers/
        ├── models/
        └── Services/

Usage

Each component can be used independently or as part of the integrated system.

Face Recognition

• Face detection and recognition capabilities
• Model training functionality
• Image capture and processing

Voice to Text

• Speech-to-text conversion
• Modular service-based architecture

RAG Model

• Two versions available:
  • V0: Basic RAG implementation
  • V1: Enhanced version with:
    • Authentication
    • Web interface
    • RESTful API endpoints

Middleware

• Handles data flow between components
• Implements data pipeline for integrated operations

Main Dependencies

• Python 3.x
• Required packages are listed in requirements.txt

Development

• Follow the modular architecture pattern
• Each component has its own README with specific instructions
• Use the existing MVC/Service patterns when adding new features